Abstract: As an advanced remanufacturing and surface engineering technology, laser cladding (LC) offers irreplaceable value in repairing damaged components and extending their service life. However, its rapid non-equilibrium processing is highly susceptible to the coupling effects of multiple process parameters. These interactions can induce defects such as porosity, cracks, and lack of fusion, which severely compromise both the service performance and reliability of the components. Consequently, online monitoring technology for real-time perception of the LC processes and early defect warning has become a key focus of current research. Through a comprehensive literature review in this field, the study summarizes the causes and key influencing factors of LC defects. It focuses on exploring the real-time monitoring of molten pool and temperature changes during the LC process by integrating multi-source sensing signals including visual, thermal imaging, acoustic emission, and spectral data. Combined with artificial intelligence models for defect classification and identification, this work aims to achieve refined control for cladding layer quality. Finally, future development prospects are outlined for the intelligent and automated online monitoring and control technologies for LC, so as to enhance the reliability of LC technology in advanced equipment remanufacturing processes.